title: Automatic detection of microcalcifications in breast ultrasound.
abstract: OBJECTIVE
  In an ultrasound (US) image, the presence of microcalcifications within breast lesions is an important indicator of malignancy. The purpose of this study was to develop a novel automatic detection system to find microcalcifications inside a breast lesion using an US image.
  
  
  METHODS
  Breast US images from 103 cases with microcalcifications were obtained using an US system with a 6-14 MHz transducer, and 585 microcalcification foci marked on 103 breast US images by a radiologist were used as the ground truth. After segmentation of the lesion contour using the level set method, the microcalcification candidates inside the lesion were found using adaptive speckle reduction and top hat filters. Then, three criteria were used to identify the real microcalcifications, including the mean, single point, and brightness criteria.
  
  
  RESULTS
  The proposed method revealed microcalcifications within the lesions in all 103 cases. The sensitivity and the false positive (FP) rate for the detection of microcalcification foci were 80.3% (470/585) and 3.1 per case, respectively. The sensitivities and FP rates for the benign and malignant cases were 79.2% (243/307) with a FP rate of 3.5 and 81.7% (227/278) with a FP rate of 2.6, respectively.
  
  
  CONCLUSIONS
  The authors' proposed method has the potential to provide a tool to help physicians detect microcalcifications within breast lesions.
authors.affiliation: Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan and Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.
authors.forename: Ruey-Feng
authors.initials: RF
authors.lastname: Chang
authors.forename: Yu-Ling
authors.initials: YL
authors.lastname: Hou
authors.forename: Chiun-Sheng
authors.initials: CS
authors.lastname: Huang
authors.forename: Jeon-Hor
authors.initials: JH
authors.lastname: Chen
authors.forename: Jung Min
authors.initials: JM
authors.lastname: Chang
authors.forename: Woo Kyung
authors.initials: WK
authors.lastname: Moon
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001331:Automation; D002114:Calcinosis / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D061705:Image-Guided Biopsy; D008875:Middle Aged; D011859:Radiography; D016217:Ultrasonography, Mammary / Q000379:methods*; D055815:Young Adult